Semi-supervised learning methods

被引：0
作者：
Liu, Jian-Wei [1 ]
Liu, Yuan [1 ]
Luo, Xiong-Lin [1 ]
机构：
[1] Research Institute of Automation, China University of Petroleum, Beijing
来源：
Jisuanji Xuebao/Chinese Journal of Computers | 2015年 / 38卷 / 08期
基金：
中国国家自然科学基金;
关键词：
Label; Labeled examples; Pair-wise constraints; Semi-supervised learning; Unlabeled instances;
D O I：
10.11897/SP.J.1016.2015.01592
中图分类号：
TP181 [自动推理、机器学习];
学科分类号：
摘要：
Semi-supervised learning is used to study how to improve performance in the presence of both examples and instances, and it has become a hot area of machine learning field. In view of the theoretical significance and practical value of semi-supervised learning, semi-supervised learning methods were reviewed in this paper systematically. Firstly, some concepts about semi-supervised learning were summarized, including definition of semi-supervised learning, development of research, assumptions relied on semi-supervised learning methods and classification of semi-supervised learning. Secondly, semi-supervised learning methods were detailed from four aspects, including classification, regression, clustering, and dimension reduction. Thirdly, theoretical analysis on semi-supervised learning was studied, and error bounds and sample complexity were given. Finally, the future research on semi-supervised learning was discussed. ©, 2015, Science Press. All right reserved.
引用
收藏
页码：1592 / 1617
页数：25
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